{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from magenta.models.rl_tuner import rl_tuner\n",
    "from magenta.models.rl_tuner import rl_tuner_ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Place to save your model checkpoints and composi|\n",
    "SAVE_PATH = \"/tmp/rl_tuner/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Model parameter settings\n",
    "ALGORITHM = 'q'\n",
    "REWARD_SCALER = 1\n",
    "OUTPUT_EVERY_NTH = 50000\n",
    "NUM_NOTES_IN_COMPOSITION = 32\n",
    "PRIME_WITH_MIDI = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rl_tuner_hparams = tf.contrib.training.HParams(random_action_probability=0.1,\n",
    "                                               store_every_nth=1,\n",
    "                                               train_every_nth=5,\n",
    "                                               minibatch_size=32,\n",
    "                                               discount_rate=0.5,\n",
    "                                               max_experience=100000,\n",
    "                                               target_network_update_rate=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reload(rl_tuner_ops)\n",
    "reload(rl_tuner)\n",
    "rl_tuner.reload_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving checkpoint of Note RNN from Magenta download server.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.LSTMCell object at 0x10e508090>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.LSTMCell object at 0x11297c1d0>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.LSTMCell object at 0x112b3bf90>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
      "WARNING:tensorflow:Can't find checkpoint file, using /Users/fjord/m/magenta-personal/magenta/models/rl_tuner/note_rnn.ckpt\n",
      "WARNING:tensorflow:Can't find checkpoint file, using /Users/fjord/m/magenta-personal/magenta/models/rl_tuner/note_rnn.ckpt\n",
      "WARNING:tensorflow:Can't find checkpoint file, using /Users/fjord/m/magenta-personal/magenta/models/rl_tuner/note_rnn.ckpt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Successfully initialized internal nets from checkpoint!\n"
     ]
    }
   ],
   "source": [
    "rl_net = rl_tuner.RLTuner(SAVE_PATH, \n",
    "                          dqn_hparams=rl_tuner_hparams, \n",
    "                          algorithm=ALGORITHM,\n",
    "                          reward_scaler=REWARD_SCALER,\n",
    "                          output_every_nth=OUTPUT_EVERY_NTH,\n",
    "                          num_notes_in_melody=NUM_NOTES_IN_COMPOSITION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated sequence: [0, 23, 0, 23, 0, 0, 0, 18, 0, 0, 0, 18, 0, 18, 0, 0, 0, 18, 0, 18, 0, 22, 0, 0, 0, 23, 0, 0, 0, 23, 0, 18]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAFyCAYAAABiC8hIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3Xm4HGWZ9/HvLyELBJOIyOKwiEJYRCEJL4KyxMFLGBFE\niYHIDIvjKIiI8R3NK6gwjoqKIovgiNvAqEEIouiwKCo4AZQxC3tAdtn3JJCQkJz7/aPqwHMO51T1\n6e7T1d35fa6rr5yup6r67jqd+zxV9fT9KCIwM7PMiKoDMDNrJ06KZmYJJ0Uzs4SToplZwknRzCzh\npGhmlnBSNDNLOCmamSXWqTqA4SDpNcC+wH3AC9VGY2ZtYCzweuDKiHiqaMWuTIpkCfEnVQdhZm3n\nMOCnRSt0a1K8D+DHPziX7bedBMCs2Sfwra99pcqYSjnG5mj3GNs9Pui+GG+/407+8Z8/AnluKNKt\nSfEFgO23ncSUyTsDMGHC+Jd+bleOsTnaPcZ2jw+6OsbSy2m+0WJmlnBSNDNLOCmamSXWmqQ48wPT\nqw6hlGNsjnaPsd3jg7U7RnVjkVlJU4D58+dd3fYXi81s+C1YuIipe0wDmBoRC4rWXWt6imZmtXBS\nNDNLOCmamSWcFM3MEk6KZmYJJ0Uzs0TlSVHS0ZJulLQkf1wnab+k/WpJPcljjaRzqozZzLpXOxSE\n+BswG7grf34k8EtJO0fE7UAA5wKfB5Svs7zVQZrZ2qHypBgR/91v0eckHQPsBtyeL1seEU+0NjIz\nWxtVfvqckjRC0qHAesB1SdNhkp6QdLOkr0hat6IQzazLVd5TBJC0I3A9WcnwZcD7IuKOvPknwP3A\nw8BbgK8Dk4D2/3KmmXWctkiKwGJgJ2AicDBwvqS9ImJxRHw/We9WSY8CV0naKiLurSJYM+tebZEU\nI2I1cE/+dIGkXYHjgWMGWP3PZDdctgYKk+Ks2ScwYcL4PstmfmA6M2e4k2nWreZcOJc5F83ts2zJ\nkqU1b9+WVXIk/Q64PyI+NEDb24E/AjtFxC2DbO8qOWb2kqFUyam8pyjpy8DlZENzXkU229bewLsk\nvQH4IHAZ8BTZKfZpwDWDJUQzs0ZUnhSBjYHzgU2BJcBNwLsi4veSNgPeSXYqPY4scV4EfLmiWM2s\ny1WeFCPiwwVtDwLTWheNma3t2mqcoplZ1ZwUzcwSTopmZgknRTOzhJOimVnCSdHMLOGkaGaWcFI0\nM0s4KZqZJZwUzcwSTopmZgknRTOzhJOimVnCSdHMLOGkaGaWcFI0M0s4KZqZJZwUzcwSTopmZgkn\nRTOzhJOimVnCSdHMLOGkaGaWqHzeZzOrXkQUtktqaPtm7KNs+2ZxT9HMLOGkaGaWcFI0M0s4KZqZ\nJZwUzcwSTopmZonKk6KkoyXdKGlJ/rhO0n5J+xhJZ0t6UtIySXMlbVRlzGbWvSpPisDfgNnA1Pzx\ne+CXkrbP208H9gcOBvYCXgdcXEGcZrYWqHzwdkT8d79Fn5N0DLCbpIeADwGHRsQ1AJKOAm6XtGtE\n3NDicM06Ui2Dqxvavob9l65Rto+1cfC2pBGSDgXWA64n6zmuA/yud52IuAN4ANi9kiDNrKtV3lME\nkLQjWRIcCywD3hcRiyVNBlZFxNJ+mzwGbNLiMM1sLdAWSRFYDOwETCS7dni+pL0K1hc19MbNzIaq\nLZJiRKwG7smfLpC0K3A8cCEwWtL4fr3Fjch6i4VmzT6BCRPG91k28wPTmTljenMCN7O2M+fCucy5\naG6fZUuW9D/ZHJwavQA7HCT9Drgf+CTwBNmNlkvytklkPcvdBrvRImkKMH/+vKuZMnnnFkVt1r6G\n/f95Lfsvu1FSViVnRP23QBYsXMTUPaYBTI2IBUXrVt5TlPRl4HKyoTmvAg4D9gbeFRFLJf0AOE3S\nM2TXG88ErvWdZzMbDpUnRWBj4HxgU2AJcBNZQvx93j4LWAPMBcYAVwDHVhCnma0FKk+KEfHhkvaV\nwHH5o5mvW9heS0HL4S7M2SlFOa39DfdnoZaT89LPc6MxFPx/GMq+22qcoplZ1ZwUzcwSTopmZgkn\nRTOzhJOimVnCSdHMLOGkaGaWqHyc4nAKCsYuRU/JtjX8vehZU7yPESOHd/uS94BKtjerUTO+Jtho\nTcZWfSHZPUUzs4SToplZwknRzCzhpGhmlnBSNDNLOCmamSWcFM3MEl09TrGQiv8e1FRPsWQcYWn9\nuEa3b4O/aa7puHZotLZnTftocPvCbYewbvX/q8zM2oiToplZwknRzCzhpGhmlnBSNDNLOCmamSWc\nFM3MEt09TjFi8Bptq1cWbzpqbPn+16wu3sfIksPbaE3HknqMlL0+zamTV6WmjI9r8/m72yWGkh2U\nr9LoPhp4D5732cysTk6KZmYJJ0Uzs4SToplZwknRzCzhpGhmlqg8KUr6rKQbJC2V9JikSyRN6rfO\n1ZJ6kscaSedUFbOZda92GKe4J3AW8BeyeE4BfiNp+4hYka8TwLnA53m5NNry0j1Lg49t6imZM7kW\nPcXjFEvHCZbFMLLkb9Zwjz2rRYNjy8p33/nzDbfkPTQ6ZvbFF4rby8btvvBccTvAuq8qbl9V/F86\nxowr3n7NiwVtJf9XE5UnxYh4d/pc0pHA48BUYF7StDwinmhhaGa2Fqr89HkAE8n+OD/db/lhkp6Q\ndLOkr0hat4LYzKzLDbmnKOlk4EcRcX+zg1H2XaTTgXkRcVvS9BPgfuBh4C3A14FJwPRmx2Bma7d6\nTp8PAj4n6RrgB8DPI6LkgkTNzgF2AN6eLoyI7ydPb5X0KHCVpK0i4t7BdjZr9glMnDC+z7JDpx/M\nzBnOpWbdas7cn3PB3Ev6LHt2ydKat1c9F4ElTQaOAmaSJdYLgB9GxP8OeWcv7/PbwAHAnhHxQMm6\n6wHPAftGxG8HaJ8CzP/LvKuZsvNOA+9kZcl9mjHrlQfd6MXpsou/DRaUKJucqynKChGMaCyGbrhZ\n1BKNfpYa/SyvWFbcDuU3WlY+X9zewI2WBYtuYpe93wkwNSIWFO2mrk9sRCyMiE8ArwP+GdgMuDa/\n3ne8pAlD2V+eEN8LvKMsIeYmk113fGSIoZuZFWq0KyFgFDA6//lp4OPA3yQdUtMOsvGGhwEfBJ6X\ntHH+GJu3v0HS5yRNkbSlpAOB84BrIuKWBuM3M+ujrqQoaWreu3sE+BawENg+IvaOiG2AE4Eza9zd\n0cB44GqyGym9jxl5+yrgncCVwO3AqcBFwIH1xG5mVqSeu883AdsDvyE7df5VRPSvdjoHOKOW/UVE\nYWKOiAeBaUONs9ToJozoqaUQbZEaisAWKrlmWMvk4Q0XJy19hWF+/TaYhL0t3kPJZ6l0+5LPcun2\nZdcLa1FyzbD8GIwavHEI/9fq+V95EdlNlYcGWyEinqQ9x0CamRWqJ3EJeOYVC6V1JX2h8ZDMzKpT\nT1I8CVh/gOXr5W1mZh2r3p7iQBdBduKVX80zM+soNV9TlPQMWTIM4E5JaWIcSdZ7/I/mhmdm1lpD\nudHySbJe4g/JTpOXJG2rgPsi4vomxmZm1nI1J8WIOA9A0r3AdRFRULzMzKwz1ZQUJY2PiN5vVC8E\n1h2sdFeyXvUiBv1eazxwa/G2W7ypfPc/+lJhu448sbC957OHF7aPOKm4uHjPqf9a2D7ypHML24HS\n77yWjV+Lxwatx5HZeKuS119Z8vpjird//tnidiDGTSzZx5LC5hhX8q3V514xGKPv9uu/unj71auK\n24FYZ3Rhe89PTi1sH3HYp4u3v/L84u33Lf6s9sz7eWE7gHYv/r5Fzxc+XNg+8sv/Wbz9/14+eNud\nJZ/TRK09xWckbRoRjwPPMvCNlt4bMCNrfnUzszZTa1L8e16+s/yOYYrFzKxyNSXFiLhmoJ/NzLpN\nrdcU31LrDiPipvrDMTOrVq2nz4vIrheWVRjwNUUz62i1JsWSW4hmZt2h1muKTZ+kysysHdU0R0te\n7fryiHgx/3lQEXFps4KrV01ztDRjXo6yyezL5idpNIYmzI/S8BwoJcdAI4uvprSiFmGpin8PTXkP\njc5v0uj2LzxX3A4wdqA6MomiyewBiuolAqwumaNlWm1ztNR6+vwLYBOySep/UbCerymaWUer9fR5\nxEA/m5l1Gyc4M7NEvRNX7SPp15LulnRX/vM7mx2cmVmrDTkpSvoYcAWwjGxyqjOBpcBlko5tbnhm\nZq1Vz8RVJwCzIuLbybIzJV2bt53dlMjMzCpQz+nzRLKeYn+/AUpqLJmZtbd6eoqXAu8jm5Q+9V7g\n1w1H1CpldfjKavAB8eDiwnZtsUPx9k88ULz9hpsVB1AytizWHV+8PZTWUyyd27o0hpL5gButp7hy\neXE7wJj1ittXF8dQegxWFccQZWP81qwubofyeYvXKTlOpfsvGQNYppZxvWXK6kqWxRhFY2ZLxhQn\nai0I8Ynk6W3AiZKmAb3TD+wGvB34Zs2vbGbWhmrtKc7q9/wZYIf80etZ4ENAcTlqM7M2VuvgbReE\nMLO1ggdvm5kl6rnRgqTNgAOBLYA+M+pExKeaEJeZWSWGnBQl7UN2B/oeYDvgFuD1ZAVoC6tPmJm1\nu3pOn08BvhERbwZeAA4GNgeuAS4a6s4kfVbSDZKWSnpM0iWSJvVbZ4yksyU9KWmZpLmSNqojdjOz\nQvWcPm8PzMx/Xg2sGxHPSfoC8EvgO0Pc357AWcBf8nhOAX4jafuIWJGvczrwD2QJeCnZt2Yuzret\nT9k4xBrGXWmz7ep+eQBtuHnxCmX1EMeU1KerRaPj20pq5JXWQywbh1hm9IDTjw9No8dgVIMxjGhC\ntb1G91E2DrLM6JKxoK3YR9Hc2EMYh1nPkXge6P0UPQK8EeidWX7Doe4sIt6dPpd0JFndxqnAPEnj\nyYb6HNo7k6Cko4DbJe0aETfU8R7MzAZUz+nzn8gGagNcBnxT0onAD/O2Rk0kK1bbO8/0VLLk/bve\nFSLiDuABYPcmvJ6Z2Uvq6Sl+Cug9Zzop//kQ4K95W92UnWudDsyLiNvyxZsAqyJiab/VH8vbzMya\nZshJMSLuSX5+Hji6ifGcQ/YtmT1qWFdkPcpBzZp9AhMn9P3+76HTD2bmjOl1B2hm7W3ORRdzwUUX\n91n27NL+farB1TRx1YAbSruQ3XQJ4PaImF/Xjl7e37eBA4A9I+KBZPk7gKuAV6e9RUn3Ad+KiDMG\n2FdnTFxV9fbQ+ZM21bJ9g++hdPvhnsCslhga/j2WvAd1wnsYfPsFi25klz3fAU2cuCqJS5sBc8iu\nK/aWmpko6TqymyEP1rHPb5NV2dk7TYi5+WR3ufcBLsnXn0Q2cPx6zMyaqJ4bLd8HRgHbR8QGEbEB\nWY9ReduQSDoHOAz4IPC8pI3zx1iAvHf4A+A0SdMkTQV+BFzrO89m1mz13GjZG3hbfgcYyO4GSzoO\nmFfH/o4mOwW/ut/yo4Dz859nAWuAuWTDga4AGpv6oGdNcXst47Z6SurgjSgYNwXl89yqZPtGXx/K\na/mtUzy+K5Y9Vbz9+JJRWmW/h5Lxd7FqRWE7gErqKUZJDCr5LJTFoLHF9RSjhnqKKvk9NKzs7LcJ\n5RLLY2jCJa0mqCcp/o2spzjQvh4e6s5qmTI1IlYCx+UPM7NhU8/p86eBsyTtkg+h6b3pcgbwr80M\nzsys1WqtvP0MfTvY44A/A6vzvLgO2c2QHwK/aHKMZmYtU+vp8yeHNQozszZRa+Xt84Y7EDOzdlBv\nkdmRwEG8PHj7NuDSiCi5lWhm1t7qGby9NVkhiL8D7iC7WT8J+Juk/SPi7uaGaGbWOvXcfT4TuBvY\nPCKmRMRksm+X3Ju3mZl1rHoHb+8WEb2lvYiIpyT9P+DapkU23JoxELR04HODg68p2b7s+6o1iAcX\nF6+wxQ6FzXH/bYXtevNexdvfvah4+22mFm//i+8WtgMwvXh4a8/3/72wfcRHTi7e/mv9ZwDua+RJ\n5xZvf9YJhe0AI2edWrzC0ieK2ycUF6qPh+4sbNfmJQWVn36ouB1gg78rjuGxewrbtenWxftfvmTw\ntheeK942UU9PcSXwqgGWrw+sqmN/ZmZto56k+GvgXElv1ct2A/6DbEIrM7OOVU9S/ATZNcXrySau\neoHstPku4PjmhWZm1nr1FJl9Fnhvfhe6tzrObRFxV7ODMzNrtSElRUmjgMXAeyLidrLeoZlZ1xjS\n6XNEvAiMHaZYzMwqV881xbOB2ZIanCjWzKz9DHmOFkmXkE0N8BxwM9k80C+JiPc3Lbo69c7RMn/e\n1UyZvPOA65S977JJ3Juxj4bnJynRjPdQqmR+Eo0sKRLbgmM47L+HFsxT0+h7qHr7qmNYsOhGdtlj\nGgzHHC1k87JcXLqWmVkHqufu81HDEYiZWTuo+7qgpI2Abcmq5NwZEY83LSozs4oM+UaLpPGS/gt4\nCLgG+CPwkKQfS5rQ7ADNzFqpnrvP3wPeCrwHmAhMyH/eBajh2/lmZu2rntPn9wD7RkQ6nemVkv6F\nbOpRM7OOVU9P8SlgoBo9S4BnGgvHzKxa9fQUvwScJunwiHgEQNImwKlAcWG6FgsKxi6VjImqaeRa\nST3FKJlEnZXLi9tHr1vcXjIJe5RMAg/AmheL20eWTMJeUscvJm5c8vrDfAyp4TisWFbcvu5AlfIS\nJbX6omz7F18obgdiVGNfJCsdR9jo/4caantGWR+sp3g2kxhRPOa18D0MYSxqPUnxGGBr4H5JD+TL\ntiCrs/haSR99OY6YUsf+zcwqU09S9LzOZta16hm8/W/DEYiZWTuo50aLmVnXclI0M0s4KZqZJdoi\nKUraU9Klkh6S1CPpwH7tP8qXp4/LqorXzLpXIwUhRgNbAXdHRNkExmXGAYuAHzJ4WbLLgSPJ5oSB\nbAhQcYwU1GBrxrzP65SM4SszdlxDm5eNv6ulnmLp3NRlMZSMQyyNocFjWMtYzNI6fCXjCId9+xrG\nINb0u2xEo/tXyRjCWpSNSS2NYfD3MJTjN+QoJK0HnAUckS+aBNwj6SzgoYj46lD3GRFXkH9FUINH\nvzIiSmb8NjNrTD2nz6cAOwHTyKY37XUVcEgTYhrMNEmPSVos6RxJGwzja5nZWqqe/upBwCER8SdJ\n6XdnbgXe2JywXuFystPqe/PXOAW4TNLuMdw1/c1srVJPUnwtMFBB2XHU+JXhoYqIC5Ont0q6Gbib\nrLf6h8G2mzX7BCZMGN9n2cwPTGfmjOnDEaaZtYE5F85lzkVz+yxbsmRpzdvXM3HVH4GLIuIsScuA\nt0TEvfk1xW0iYr8h7fCV++8BDoqIS0vWexw4MSK+N0Bb6cRV3aAZk291egztPmFSM7avZR9WbMHC\nRUwdxomrTgAul7RDvv3xkt4E7A7sXcf+hkzSZsBrgEda8XpmtvYY8o2WvLjszmQJ8WbgXcBjwO4R\nMb+eICSNk7STpN5u3Rvy55vnbV+X9FZJW0rah6woxZ3AlfW8npnZYOoaGBQRdwP/0sQ4diG7Nhj5\n45v58vOAjwFvAQ4nm/7gYbJk+IWIKCkG2Nna4R5SaQxl9RgbHQfZhGNQXkuwuBZgaR3ARl+/Caq+\nBNAM7RAD1DdOcQ2waf/Z+yS9Bng8IoY8ijMirqG419rQdUozs1rV8ydwsHQ9BljVQCxmZpWruaco\n6RP5jwF8WFJag30ksBewuImxmZm13FBOn2fl/wo4GkgnVFgF3JcvNzPrWDUnxYjYCkDSH4D3R4Rn\n7jOzrlPPdATv6P25t3iDv2pnZt2irrEGkg7Pv2q3Algh6SZJ/9Tc0MzMWq+eITmfIpvf+dvAtWTX\nGN8O/IekDSPiW80Nce3VEV/tKpuLtxP0lMxZPLL6WsyNnoy1w8lcO8RQi3oGbx8HHBMR5yfLfinp\nVuBkwEnRzDpWPX8CNwWuG2D5dXmbmVnHqicp3gXMGGD5IcBfGwvHzKxa9Zw+nwT8TNJeZNcUA9gD\n2IeBk6WZWceop0rOxcBbgSfJqnC/P/9514i4pLnhmZm1Vr1VcuYD/9jkWMzMKlf9WAMzszYylIIQ\nPZTPwRIR0eDkra3RjNptVdewa4v3MMy1BltRyj9K5huu+vfYLjE0un2VMQxlz0NJYO8raHsb2fjF\nDhhtbGY2uKEUhPhl/2WStiObbvQA4CfA55sXmplZ69X73efXSfoecBNZYt05Io6IiAeaGp2ZWYsN\nKSlKmiDpa2QDuN8E7BMRB0TELcMSnZlZiw3lRstngNnAo8DMgU6nzcw63VButHyVrFTYXcARko4Y\naKWIeH8zAjMzq8JQkuL5DO3OtplZxxnK3ecjhzEOM7O20BEDresV1D8gtDWTsBe3l0YwzJO4Qw3v\n4cWVxduPGjO8r19yDKCG47BmdfH2JYO7G/491mDYP0vD/Po1xdDw/4eCNYbw/9lf8zMzSzgpmpkl\nnBTNzBJOimZmCSdFM7NEWyRFSXtKulTSQ5J6JB04wDpflPSwpOWSfitp6ypiNbPu1hZJERgHLAKO\nZYA775JmAx8HPgrsCjwPXClpdCuDNLPu1xbjFCPiCuAKAA1cifJ44N8j4lf5OocDj5HNEXPhYPsV\nbT6hfKOxqQ0moh89ttrXb8YxWGdUgzG0wWes6hia8foN/38YfPuh5IF26SkOStJWwCbA73qXRcRS\n4M/A7lXFZWbdqe2TIllCDLKeYeqxvM3MrGk6ISkORrhAhZk1WVtcUyzxKFkC3Ji+vcWNgIVFG86a\nfQITJozvs2zmB6Yzc8b0ZsdoZm1izoVzmXPR3D7LlixZWvP2akbhg2bKZw08KCIuTZY9DJwaEd/K\nn48nS5CHR8RFA+xjCjB//ryrmTJ55xZFbmbtasHCRUzdYxrA1IhYULRuW/QUJY0Dtubl2QDfIGkn\n4OmI+BtwOvA5SXcB9wH/DjwIuPq3mTVVWyRFYBfgD+TVvoBv5svPAz4UEV+XtB7wXWAi8D/AP0TE\nqiqCNbPu1RZJMSKuoeSmT0ScDJzcinjMbO3VyXefzcyazknRzCzhpGhmlnBSNDNLOCmamSWcFM3M\nEk6KZmYJJ0Uzs4SToplZwknRzCzhpGhmlnBSNDNLOCmamSWcFM3MEk6KZmYJJ0Uzs4SToplZwknR\nzCzhpGhmlnBSNDNLOCmamSWcFM3MEk6KZmYJJ0Uzs4SToplZwknRzCzhpGhmlnBSNDNLOCmamSWc\nFM3MEh2RFCWdJKmn3+O2quMys+6zTtUBDMEtwD6A8uerK4zFzLpUJyXF1RHxRNVBmFl364jT59w2\nkh6SdLekH0vavOqAzKz7dEpS/BNwJLAvcDSwFfBHSeOqDMrMuk9HnD5HxJXJ01sk3QDcD8wAflRN\nVGbWjToiKfYXEUsk3QlsXbTerNknMGHC+D7LZn5gOjNnTB/O8MysQnMunMuci+b2WbZkydKat1dE\nNDumYSdpfbKe4kkR8e0B2qcA8+fPu5opk3dueXxm1l4WLFzE1D2mAUyNiAVF63bENUVJp0raS9KW\nkt4GXEI2JGdOxaGZWZfplNPnzYCfAq8BngDmAbtFxFOVRmVmXacjkmJEzKw6BjNbO3TE6bOZWas4\nKZqZJZwUzcwSTopmZgknRTOzhJOimVnCSdHMLOGkaGaWcFI0M0s4KZqZJZwUzcwSTopmZgknRTOz\nhJOimVnCSdHMLNER9RTrFUAnTrfQLJJK11mbj08nKftdDvfvserXbzSGoUTnnqKZWcJJ0cws4aRo\nZpZwUjQzSzgpmpklnBTNzBJOimZmie4ep9jTQ/T0DNy49Inijce/tvwF1rxY3D5yVHF7z+ri9uee\nLW5fb3xx++ixxe1ArCmJIQY5fr0GO769Ro1pbP8q+bvds6a4HWD1quL2st/TyJL/Jg2P0ath+xEj\nS0JoMIay30PZ65d9DrK1CltFyTjEEcWfhaIYaosv456imVnCSdHMLOGkaGaWcFI0M0s4KZqZJZwU\nzcwSHZUUJR0r6V5JKyT9SdL/qTomM+suHTNOUdIhwDeBjwA3ALOAKyVNiognB9rm9L0PYGMNPL7q\nqx/bq/D1TvjOH0tjOuWq7xa2z97no4XtX7/zD4XtV731PYXt+8z5amH7x9/18cJ2gBPfsmlh+/dv\nf7yw/fO/OLWw/bj9P1XY/untNy5sP/X2xwrbz/zGEYXtAN848SfFMXzn+ML24z/0zcL2M648s7D9\nuH0/Udh+5skHF7YDHHfyxYXtZ31lZmH7J06YU7z92ccUtn/82O8Ub3/aUYXtAF+afX5h+xduvKyw\n/dgd9i1s/9YhkwdtW/X0ssJtU53UU5wFfDcizo+IxcDRwHLgQ9WGZWbdpCOSoqRRwFTgd73LIhvC\nfxWwe1VxmVn36YikCGwIjAT6n0s9BmxSyw4WR8lX8trABb+6ouoQSt3SU/KVuTZwwcI7qw6h0F/p\ngM/iX26rOoRSdw3TceyYa4qDEAVfqLw6XmBMZN+nfJQ1LI4X2U6j2E4l33WtyAW/upJDD9iv6jAK\n3dKzih1HjK46jEI/W3gnh06eVHUYg7qLF9mG9vwM9vrZX27n0F12qDqMQnfxIlsPcBwvvP9xLnyg\n77XwJatKvuOf6JSk+CSwBuh/VX4jXtl7fMk0jX3pRssvepZz0Ij1hi1AM2sPM7bciBlbbtRn2cKn\nl/H23y6safuOOH2OiBeB+cA+vcuUTe21D3BdVXGZWffplJ4iwGnAeZLm8/KQnPWA/6wyKDPrLh2T\nFCPiQkkbAl8kO41eBOwbEQMVRhwL8Hfv25/NN8zqIq53xX+z1X77v7TCom2Kx+e9/p+3LI1p4bLi\n+m9bffifird/oG/oS1as7LPshQ/OKN5+efF1qe0+Uvz6APdsMK6w/XW7r+jzfOwVv+Z1+708fnLh\nynULt9+2JIb7JxZf0th2z+WF7Ys22OYVy5aOXr/P8tcedVjhPhaOKf4sTCp5DwtXFF9j7X8M/ueK\ny9h2v3e/9HzRpm8o3B5gu48UH6dFr33lcSiKob+F6/f9vC8ZuV6fZWWfpUWv3rqwHeB1H/rH4hge\nfqawvX8M8664jO2S43jzGwavgXrXQ49AdvpcWmRU3TgZuqQPAsUjds1sbXRYRPy0aIVuTYqvAfYF\n7gNeqDbR6UefAAAIsUlEQVQaM2sDY4HXA1dGxFNFK3ZlUjQzq1dH3H02M2sVJ0Uzs4SToplZwknR\nzCyxViTFdi5OK+kkST39HpV+G1/SnpIulfRQHs+BA6zzRUkPS1ou6beSygeqtSg+ST8a4JgWF+tr\nfoyflXSDpKWSHpN0iaRJ/dYZI+lsSU9KWiZprqSNBttnBfFd3e8YrpF0Tiviy1//aEk3SlqSP66T\ntF/SPizHr+uTYlKc9iRgMnAjWXHaDSsNrK9byAakb5I/9qg2HMaRDY4/lgEKbkiaDXwc+CiwK/A8\n2TFtVaWIwvhyl9P3mBZXYW2+PYGzgLcC7wRGAb+RlI52Px3YHzgY2At4HVBcTba18QVwLi8fx02B\nz7QoPoC/AbPJygZOBX4P/FLS9nn78By/iOjqB/An4IzkuYAHgc9UHVsez0nAgqrjKIivBziw37KH\ngVnJ8/HACmBGm8T3I+DnVR+7fjFtmMe6R3LMVgLvS9bZNl9n16rjy5f9ATit6mPXL86ngKOG8/h1\ndU+xg4rTbpOfCt4t6ceSNq86oMFI2oqs15Ae06XAn2mvYzotPy1cLOkcSRtUHM9Esp7X0/nzqWRf\ns02P4x3AA1RzHPvH1+swSU9IulnSV/r1JFtG0ghJh5LVO7ieYTx+HfPd5zoVFafdtvXhDOhPwJHA\nHWSnJycDf5S0Y0Q8X2Fcg9mE7D9P3QV/W+BystOoe4E3AqcAl0naPf+j2FJ5RafTgXkR0Xu9eBNg\nVf4HJdXy4zhIfJB9VfZ+sjODtwBfByYB01sY245kSXAssIysZ7hY0mSG6fh1e1IcTGFx2laKiCuT\np7dIuoHsgziD7DSwU7TTMb0weXqrpJuBu4FpZKeErXYOsAO1XSuu4jj2xvf2dGFEfD95equkR4Gr\nJG0VEfe2KLbFwE5kPdmDgfMlFc061/Dx6+rTZ+osTluliFgC3Am07G7uED1K9sHrpGN6L9lnoeXH\nVNK3gXcD0yLi4aTpUWC0pPH9NmnpcewX3yMlq/+Z7HffsuMYEasj4p6IWBARJ5LdKD2eYTx+XZ0U\nowOL00pan+yUr+wDWok8wTxK32M6nuwuZrse082A19DiY5onnPcC74iIB/o1zwdW0/c4TgK2IDtd\nrDq+gUwm64VV+dkcAYxhOI9f1XeTWnC3agbZndHDge2A75LdwXpt1bHl8Z1KNpxgS+BtwG/J/tK9\npsKYxpGdsuxMdjfvk/nzzfP2z+TH8ADgzcAvgL8Co6uOL2/7OlmS3jL/T/MX4HZgVAuP4TnAM2RD\nXzZOHmP7rXMv2Wn9VOBa4H/aIT7gDcDngCn5cTwQuAv4fQuP4ZfJLjlsCexIdm14NfD3w3n8WvLm\nqn4AHyMrI7aC7K/ILlXHlMQ2h2yI0AqyO2c/BbaqOKa982Szpt/jh8k6J5NdgF8OXAls3Q7xkV2Q\nv4KsN/sCcA/wHVr8R3CQ+NYAhyfrjCEbK/gk2U2Ei4CN2iE+YDPgauCJ/Hd8R56U1m/hMfx+/vtb\nkf8+f9ObEIfz+Ll0mJlZoquvKZqZDZWToplZwknRzCzhpGhmlnBSNDNLOCmamSWcFM3MEk6KZmYJ\nJ0Uzs4STog07SXvn83v0r2jSqtffJ533Jp8XZ2FFsYzK5wuaUsXrWzknRWtIMqFR/4miepd/geyL\n+pvGKwuCtsrXgC/2Wzbs328dKPlGVrnpVLKiFdaG1tYis9Y8aZXjQ4F/I6vOrHzZcxGxGni81YEB\nSNqDrOLLz6t4fQZOvj8FTpO0fUTc3uqArJh7itaQiHi89wEsyRbFE8ny5fnpc0/v6bOkIyQ9I2n/\nfA6V5yVdKGndvO1eSU9LOiOvf0m+3WhJ35D0oKTnJF0vae+SEA8BfhMRq/o3SPqIpAfy1/+ZpFf1\na/+wpNuUTY17m6Rj+rV/VdId+fZ3K5v2dWTveySblGynpNd8eH7MniXrPR861ONtw889RWuV/j2m\n9YDjyOpdjgcuyR/PAP/Ay727eWQloQDOJquJOYOs0On7gMslvTki7h7kdfcEfjzA8m2AD5BNkTmB\nrOzYOcA/AUg6jKw82rFk06lOBr4n6bmI+K98H0vJ6nQ+QlZX8nv5sm8APyOrAbgvWU1Hkf3R6HVD\nHpu1m1bWmPOjux/AEcDTAyzfm6xW3/hkvTXA65N1vkNWE2/dZNnlwDn5z1sALwKb9Nv3b4EvFcT0\nDHBYv2UnAavIrnP2LtuXrIDpRvnzvwKH9NvuRODagtf6v8AN/V5nwOlryf4g3F3178yPVz7cU7Sq\nLI+I+5LnjwH3RcSKfss2yn/ekWxmxjvTU2pgNFmR0cGsS1Zstr8Hou+cJNeTXU7aVtJzZFNC/EBS\nOnnTSODZ3ieSDiFLbm8E1ic780p7g0VWkPWWrc04KVpVXuz3PAZZ1nvde32yntwUsqrRqecKXudJ\n4NU1xBPJv+vnP3+Y7DQ3tQZA0u5kp+WfJ6sIvQSYCXyqhtcC2ICsqrW1GSdF6xQLyXpqG0fEtUPc\nbocBlm8haZOIeDR//jayhHdHRDwh6SHgjRFxwSD73Z2sZ/vV3gWSXt9vnVV5zAPZMY/N2oyTorWK\nylcZXET8VdJPyeb9/VeyhLIR8PfAjRFx+SCbXkl2M6S/lcB5kj5NdqPlDOBnEdHbezsZOEPSUrI5\nX8YAuwATI+J0smuOW+Sn0P8LvAc4qN9r3AdsJWknsnl4lsXLd8H3JLtGaW3GQ3KsVZoxWPpI4Hyy\nu7uLye5W70I24ddgfgK8SdI2/Zb/lezu9mVkSW8R2Z3mLNiIH5CdPh8F3EQ2idMRZLPHERG/Ar5F\nNnHSQmA3XjlA/OJ8338gG6d5KLx06j0+b7c244mrrOtJ+hrZne9jSlduAUkXAAsj4mtVx2Kv5J6i\nrQ2+Atzf7651JSSNIut5nl51LDYw9xTNzBLuKZqZJZwUzcwSTopmZgknRTOzhJOimVnCSdHMLOGk\naGaWcFI0M0s4KZqZJf4/57G0kR1yYHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e54750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate initial music sequence before training with RL\n",
    "rl_net.generate_music_sequence(visualize_probs=True, title='pre_rl', length=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating initial model...\n",
      "Evaluating model...\n",
      "Training iteration 50000\n",
      "\tReward for last 50000 steps: -271440.762102\n",
      "\t\tMusic theory reward: -19212.4007021\n",
      "\t\tNote RNN reward: -252228.3614\n",
      "\tExploration probability is 0.9042382\n",
      "Evaluating model...\n",
      "Training iteration 100000\n",
      "\tReward for last 50000 steps: -252624.961234\n",
      "\t\tMusic theory reward: -8962.16737395\n",
      "\t\tNote RNN reward: -243662.79386\n",
      "\tExploration probability is 0.8084782\n",
      "Evaluating model...\n",
      "Training iteration 150000\n",
      "\tReward for last 50000 steps: -231745.239523\n",
      "\t\tMusic theory reward: 1347.95386468\n",
      "\t\tNote RNN reward: -233093.193388\n",
      "\tExploration probability is 0.7127182\n",
      "Evaluating model...\n",
      "Training iteration 200000\n",
      "\tReward for last 50000 steps: -215197.618369\n",
      "\t\tMusic theory reward: 3926.74782733\n",
      "\t\tNote RNN reward: -219124.366196\n",
      "\tExploration probability is 0.6169582\n",
      "Evaluating model...\n",
      "Training iteration 250000\n",
      "\tReward for last 50000 steps: -195172.008609\n",
      "\t\tMusic theory reward: 8494.54816083\n",
      "\t\tNote RNN reward: -203666.556769\n",
      "\tExploration probability is 0.5211982\n",
      "Evaluating model...\n",
      "Training iteration 300000\n",
      "\tReward for last 50000 steps: -172458.443775\n",
      "\t\tMusic theory reward: 12818.4484198\n",
      "\t\tNote RNN reward: -185276.892194\n",
      "\tExploration probability is 0.4254382\n",
      "Evaluating model...\n",
      "Training iteration 350000\n",
      "\tReward for last 50000 steps: -145884.029703\n",
      "\t\tMusic theory reward: 19940.1936502\n",
      "\t\tNote RNN reward: -165824.223353\n",
      "\tExploration probability is 0.3296782\n",
      "Evaluating model...\n",
      "Training iteration 400000\n",
      "\tReward for last 50000 steps: -123316.505537\n",
      "\t\tMusic theory reward: 23591.0168802\n",
      "\t\tNote RNN reward: -146907.522418\n",
      "\tExploration probability is 0.2339182\n",
      "Evaluating model...\n",
      "Training iteration 450000\n",
      "\tReward for last 50000 steps: -92492.2224385\n",
      "\t\tMusic theory reward: 35479.3464614\n",
      "\t\tNote RNN reward: -127971.5689\n",
      "\tExploration probability is 0.1381582\n",
      "Evaluating model...\n",
      "Training iteration 500000\n",
      "\tReward for last 50000 steps: -69201.482589\n",
      "\t\tMusic theory reward: 37484.8345059\n",
      "\t\tNote RNN reward: -106686.317095\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 550000\n",
      "\tReward for last 50000 steps: -68216.0683477\n",
      "\t\tMusic theory reward: 35826.0362874\n",
      "\t\tNote RNN reward: -104042.104635\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 600000\n",
      "\tReward for last 50000 steps: -66148.9597383\n",
      "\t\tMusic theory reward: 35287.3891036\n",
      "\t\tNote RNN reward: -101436.348842\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 650000\n",
      "\tReward for last 50000 steps: -61028.822896\n",
      "\t\tMusic theory reward: 41125.9112819\n",
      "\t\tNote RNN reward: -102154.734178\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 700000\n",
      "\tReward for last 50000 steps: -53099.5787372\n",
      "\t\tMusic theory reward: 48232.8142846\n",
      "\t\tNote RNN reward: -101332.393022\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 750000\n",
      "\tReward for last 50000 steps: -56647.105211\n",
      "\t\tMusic theory reward: 44129.6532664\n",
      "\t\tNote RNN reward: -100776.758477\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 800000\n",
      "\tReward for last 50000 steps: -62086.1018061\n",
      "\t\tMusic theory reward: 38124.7880191\n",
      "\t\tNote RNN reward: -100210.889825\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 850000\n",
      "\tReward for last 50000 steps: -59706.0010477\n",
      "\t\tMusic theory reward: 41414.4748665\n",
      "\t\tNote RNN reward: -101120.475914\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 900000\n",
      "\tReward for last 50000 steps: -57400.849034\n",
      "\t\tMusic theory reward: 43818.3817594\n",
      "\t\tNote RNN reward: -101219.230793\n",
      "\tExploration probability is 0.1\n",
      "Evaluating model...\n",
      "Training iteration 950000\n",
      "\tReward for last 50000 steps: -62722.6680829\n",
      "\t\tMusic theory reward: 37326.3583895\n",
      "\t\tNote RNN reward: -100049.026472\n",
      "\tExploration probability is 0.1\n"
     ]
    }
   ],
   "source": [
    "rl_net.train(num_steps=1000000, exploration_period=500000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAF5CAYAAABHi4TvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62d6e95b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the rewards received during training. Improves as chance of random exploration action decreases.\n",
    "rl_net.plot_rewards()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAF5CAYAAAB5ruuhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f628d1539d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot rewards received during calls to evaluation function throughout training. \n",
    "# Does not include exploration or random actions.\n",
    "rl_net.plot_evaluation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated sequence: [0, 0, 1, 23, 0, 23, 26, 0, 23, 25, 0, 26, 0, 0, 1, 30, 0, 23, 26, 0, 33, 0, 28, 26, 0, 0, 21, 0, 0, 26, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAFyCAYAAABiC8hIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62d6d87310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rl_net.generate_music_sequence(visualize_probs=True, title='post_rl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# If you're happy with the model, save a version!\n",
    "rl_net.save_model(SAVE_PATH, 'my_cool_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total compositions: 100.0\n",
      "Total notes:3200.0\n",
      "\tCompositions starting with tonic: 5.0\n",
      "\tCompositions with unique highest note:63.0\n",
      "\tCompositions with unique lowest note:63.0\n",
      "\tNumber of resolved leaps:47.0\n",
      "\tNumber of double leaps:28.0\n",
      "\tNotes not in key:58.0\n",
      "\tNotes in motif:2113.0\n",
      "\tNotes in repeated motif:0.0\n",
      "\tNotes excessively repeated:0.0\n",
      "\n",
      "\tPercent compositions starting with tonic:0.05\n",
      "\tPercent compositions with unique highest note:0.63\n",
      "\tPercent compositions with unique lowest note:0.63\n",
      "\tPercent of leaps resolved:0.626666666667\n",
      "\tPercent notes not in key:0.018125\n",
      "\tPercent notes in motif:0.6603125\n",
      "\tPercent notes in repeated motif:0.0\n",
      "\tPercent notes excessively repeated:0.0\n",
      "\n",
      "\tAverage autocorrelation of lag1:-0.331231351539\n",
      "\tAverage autocorrelation of lag2:0.108201559\n",
      "\tAverage autocorrelation of lag3:-0.0560363994806\n",
      "\n",
      "\tAvg. num octave jumps per composition:0.02\n",
      "\tAvg. num sevenths per composition:0.03\n",
      "\tAvg. num fifths per composition:0.56\n",
      "\tAvg. num sixths per composition:0.36\n",
      "\tAvg. num fourths per composition:2.28\n",
      "\tAvg. num rest intervals per composition:7.18\n",
      "\tAvg. num seconds per composition:3.34\n",
      "\tAvg. num thirds per composition:0.55\n",
      "\tAvg. num in key preferred intervals per composition:1.25\n",
      "\tAvg. num special rest intervals per composition:6.47\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute statistics about how well the model adheres to the music theory rules.\n",
    "stat_dict = rl_net.evaluate_music_theory_metrics(num_compositions=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
